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Contents

Interaction clustering, PID and primary particles

Imports and configuration

The usual imports, click to see the details.

import numpy as np
import yaml
import torch
import plotly
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=False)

import sys, os
# set software directory
software_dir = '%s/lartpc_mlreco3d' % os.environ.get('HOME')
sys.path.insert(0,software_dir)

from mlreco.visualization import scatter_points, plotly_layout3d
from mlreco.visualization.gnn import scatter_clusters, network_topology, network_schematic
from mlreco.utils.ppn import uresnet_ppn_type_point_selector
from mlreco.utils.cluster.dense_cluster import fit_predict_np, gaussian_kernel
from mlreco.main_funcs import process_config, prepare
from mlreco.utils.gnn.cluster import get_cluster_label
from mlreco.utils.deghosting import adapt_labels_numpy as adapt_labels
from mlreco.visualization.gnn import network_topology


from larcv import larcv

/usr/local/lib/python3.6/dist-packages/MinkowskiEngine/__init__.py:42: UserWarning:

The environment variable `OMP_NUM_THREADS` not set. MinkowskiEngine will automatically set `OMP_NUM_THREADS=16`. If you want to set `OMP_NUM_THREADS` manually, please export it on the command line before running a python script. e.g. `export OMP_NUM_THREADS=12; python your_program.py`. It is recommended to set it below 24.

Welcome to JupyROOT 6.22/02

And the configuration which is loaded from the file inference.cfg:

cfg=yaml.load(open('./inference.cfg', 'r'),Loader=yaml.Loader)
# pre-process configuration (checks + certain non-specified default settings)
process_config(cfg)
# prepare function configures necessary "handlers"
hs=prepare(cfg)

Config processed at: Linux volt003 3.10.0-1160.21.1.el7.x86_64 #1 SMP Tue Mar 16 18:28:22 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

$CUDA_VISIBLE_DEVICES="0"

{   'iotool': {   'batch_size': 4,
                  'collate_fn': 'CollateSparse',
                  'dataset': {   'data_keys': [   '/gpfs/slac/staas/fs1/g/neutrino/kterao/data/wire_mpvmpr_2020_04/test_*.root'],
                                 'limit_num_files': 10,
                                 'name': 'LArCVDataset',
                                 'schema': {   'cluster_label': [   'parse_cluster3d_clean_full',
                                                                    'cluster3d_pcluster',
                                                                    'particle_pcluster',
                                                                    'particle_mpv',
                                                                    'sparse3d_pcluster_semantics'],
                                               'input_data': [   'parse_sparse3d_scn',
                                                                 'sparse3d_reco',
                                                                 'sparse3d_reco_chi2'],
                                               'kinematics_label': [   'parse_cluster3d_kinematics_clean',
                                                                       'cluster3d_pcluster',
                                                                       'particle_corrected',
                                                                       'particle_mpv',
                                                                       'sparse3d_pcluster_semantics'],
                                               'particle_graph': [   'parse_particle_graph_corrected',
                                                                     'particle_corrected',
                                                                     'cluster3d_pcluster'],
                                               'particles_asis': [   'parse_particle_asis',
                                                                     'particle_pcluster',
                                                                     'cluster3d_pcluster'],
                                               'particles_label': [   'parse_particle_points_with_tagging',
                                                                      'sparse3d_pcluster',
                                                                      'particle_corrected'],
                                               'segment_label': [   'parse_sparse3d_scn',
                                                                    'sparse3d_pcluster_semantics_ghost']}},
                  'minibatch_size': 4,
                  'num_workers': 4,
                  'shuffle': False},
    'model': {   'loss_input': [   'segment_label',
                                   'particles_label',
                                   'cluster_label',
                                   'kinematics_label',
                                   'particle_graph'],
                 'modules': {   'chain': {   'enable_cnn_clust': True,
                                             'enable_cosmic': False,
                                             'enable_ghost': True,
                                             'enable_gnn_inter': True,
                                             'enable_gnn_kinematics': False,
                                             'enable_gnn_particle': False,
                                             'enable_gnn_shower': True,
                                             'enable_gnn_track': True,
                                             'enable_ppn': True,
                                             'enable_uresnet': True,
                                             'use_ppn_in_gnn': True,
                                             'verbose': True},
                                'cosmic_discriminator': {   'network_base': {   'data_dim': 3,
                                                                                'features': 4,
                                                                                'leakiness': 0.33,
                                                                                'spatial_size': 768},
                                                            'res_encoder': {   'coordConv': True,
                                                                               'latent_size': 2,
                                                                               'pool_mode': 'avg'},
                                                            'uresnet_encoder': {   'features': 16,
                                                                                   'filters': 16,
                                                                                   'num_strides': 9},
                                                            'use_input_data': False,
                                                            'use_true_interactions': False},
                                'cosmic_loss': {   'node_loss': {   'balance_classes': True,
                                                                    'name': 'type',
                                                                    'target_col': 8}},
                                'dbscan_frag': {   'cluster_classes': [0, 2, 3],
                                                   'delta_label': 3,
                                                   'dim': 3,
                                                   'eps': [   1.999,
                                                              3.999,
                                                              1.999,
                                                              4.999],
                                                   'michel_label': 2,
                                                   'min_samples': 1,
                                                   'min_size': [3, 3, 3, 3],
                                                   'num_classes': 4,
                                                   'ppn_distance_threshold': 1.999,
                                                   'ppn_mask_radius': 5,
                                                   'ppn_score_threshold': 0.9,
                                                   'ppn_type_threshold': 0.3,
                                                   'track_clustering_method': 'closest_path',
                                                   'track_label': 1},
                                'full_chain_loss': {   'clustering_weight': 1.0,
                                                       'cosmic_weight': 1.0,
                                                       'flow_weight': 1.0,
                                                       'inter_gnn_weight': 1.0,
                                                       'kinematics_p_weight': 1.0,
                                                       'kinematics_type_weight': 1.0,
                                                       'kinematics_weight': 1.0,
                                                       'particle_gnn_weight': 1.0,
                                                       'ppn_weight': 1.0,
                                                       'segmentation_weight': 1.0,
                                                       'shower_gnn_weight': 1.0,
                                                       'track_gnn_weight': 1.0},
                                'grappa_inter': {   'base': {   'add_start_dir': True,
                                                                'add_start_point': True,
                                                                'group_pred': 'score',
                                                                'kinematics_mlp': True,
                                                                'kinematics_momentum': False,
                                                                'kinematics_type': True,
                                                                'node_min_size': 3,
                                                                'node_type': [   0,
                                                                                 1,
                                                                                 2,
                                                                                 3],
                                                                'start_dir_max_dist': 5,
                                                                'vertex_mlp': True},
                                                    'edge_encoder': {   'name': 'geo',
                                                                        'use_numpy': False},
                                                    'gnn_model': {   'aggr': 'add',
                                                                     'edge_classes': 2,
                                                                     'edge_feats': 19,
                                                                     'edge_output_feats': 64,
                                                                     'leakiness': 0.1,
                                                                     'name': 'meta',
                                                                     'node_classes': 2,
                                                                     'node_feats': 28,
                                                                     'node_output_feats': 64,
                                                                     'num_mp': 3},
                                                    'node_encoder': {   'name': 'geo',
                                                                        'use_numpy': False},
                                                    'type_net': {   'num_hidden': 32},
                                                    'vertex_net': {   'num_hidden': 32}},
                                'grappa_inter_loss': {   'edge_loss': {   'balance_classes': False,
                                                                          'high_purity': False,
                                                                          'loss': 'CE',
                                                                          'name': 'channel',
                                                                          'reduction': 'sum',
                                                                          'source_col': 6,
                                                                          'target': 'group',
                                                                          'target_col': 7},
                                                         'node_loss': {   'balance_classes': True,
                                                                          'name': 'kinematics',
                                                                          'spatial_size': 768,
                                                                          'type_loss': 'CE'}},
                                'grappa_kinematics': {   'base': {   'edge_dist_metric': 'set',
                                                                     'edge_dist_numpy': True,
                                                                     'edge_max_dist': -1,
                                                                     'kinematics_mlp': True,
                                                                     'kinematics_momentum': True,
                                                                     'kinematics_type': False,
                                                                     'network': 'complete',
                                                                     'node_min_size': -1,
                                                                     'node_type': -1},
                                                         'edge_encoder': {   'cnn_encoder': {   'name': 'cnn2',
                                                                                                'network_base': {   'data_dim': 3,
                                                                                                                    'features': 4,
                                                                                                                    'leakiness': 0.33,
                                                                                                                    'spatial_size': 768},
                                                                                                'res_encoder': {   'coordConv': True,
                                                                                                                   'latent_size': 32,
                                                                                                                   'pool_mode': 'avg'},
                                                                                                'uresnet_encoder': {   'filters': 32,
                                                                                                                       'input_kernel': 3,
                                                                                                                       'num_classes': 5,
                                                                                                                       'num_filters': 32,
                                                                                                                       'num_strides': 9,
                                                                                                                       'reps': 2}},
                                                                             'geo_encoder': {   'more_feats': True},
                                                                             'name': 'mix_debug',
                                                                             'normalize': True},
                                                         'gnn_model': {   'aggr': 'add',
                                                                          'edge_classes': 2,
                                                                          'edge_feats': 51,
                                                                          'edge_output_feats': 64,
                                                                          'leak': 0.33,
                                                                          'name': 'nnconv_old',
                                                                          'node_classes': 5,
                                                                          'node_feats': 83,
                                                                          'node_output_feats': 128,
                                                                          'num_mp': 3},
                                                         'momentum_net': {   'num_hidden': 32},
                                                         'node_encoder': {   'cnn_encoder': {   'name': 'cnn2',
                                                                                                'network_base': {   'data_dim': 3,
                                                                                                                    'features': 4,
                                                                                                                    'leakiness': 0.33,
                                                                                                                    'spatial_size': 768},
                                                                                                'res_encoder': {   'coordConv': True,
                                                                                                                   'latent_size': 64,
                                                                                                                   'pool_mode': 'avg'},
                                                                                                'uresnet_encoder': {   'filters': 32,
                                                                                                                       'input_kernel': 3,
                                                                                                                       'num_classes': 5,
                                                                                                                       'num_filters': 16,
                                                                                                                       'num_strides': 9,
                                                                                                                       'reps': 2}},
                                                                             'geo_encoder': {   'more_feats': True},
                                                                             'name': 'mix_debug',
                                                                             'normalize': True},
                                                         'use_true_particles': False},
                                'grappa_kinematics_loss': {   'edge_loss': {   'balance_classes': False,
                                                                               'high_purity': False,
                                                                               'name': 'channel',
                                                                               'reduction': 'sum',
                                                                               'target': 'particle_forest'},
                                                              'node_loss': {   'name': 'kinematics',
                                                                               'reg_loss': 'l2'}},
                                'grappa_particle': {   'base': {   'node_min_size': 10,
                                                                   'node_type': -1},
                                                       'edge_encoder': {   'name': 'geo',
                                                                           'use_numpy': True},
                                                       'gnn_model': {   'aggr': 'add',
                                                                        'edge_classes': 2,
                                                                        'edge_feats': 19,
                                                                        'edge_output_feats': 64,
                                                                        'leakiness': 0.1,
                                                                        'name': 'meta',
                                                                        'node_classes': 2,
                                                                        'node_feats': 24,
                                                                        'node_output_feats': 64,
                                                                        'num_mp': 3},
                                                       'model_name': 'particle_gnn',
                                                       'node_encoder': {   'name': 'geo',
                                                                           'use_numpy': True}},
                                'grappa_particle_loss': {   'edge_loss': {   'balance_classes': False,
                                                                             'high_purity': True,
                                                                             'loss': 'CE',
                                                                             'name': 'channel',
                                                                             'reduction': 'sum',
                                                                             'source_col': 5,
                                                                             'target': 'group',
                                                                             'target_col': 6},
                                                            'node_loss': {   'balance_classes': False,
                                                                             'group_pred_alg': 'score',
                                                                             'high_purity': True,
                                                                             'loss': 'CE',
                                                                             'name': 'primary',
                                                                             'reduction': 'sum',
                                                                             'use_group_pred': True}},
                                'grappa_shower': {   'base': {   'add_start_dir': True,
                                                                 'add_start_point': True,
                                                                 'node_min_size': 3,
                                                                 'node_type': 0,
                                                                 'start_dir_max_dist': 5},
                                                     'edge_encoder': {   'name': 'geo',
                                                                         'use_numpy': False},
                                                     'gnn_model': {   'aggr': 'add',
                                                                      'edge_classes': 2,
                                                                      'edge_feats': 19,
                                                                      'edge_output_feats': 64,
                                                                      'leakiness': 0.1,
                                                                      'name': 'meta',
                                                                      'node_classes': 2,
                                                                      'node_feats': 28,
                                                                      'node_output_feats': 64,
                                                                      'num_mp': 3},
                                                     'node_encoder': {   'name': 'geo',
                                                                         'use_numpy': False}},
                                'grappa_shower_loss': {   'edge_loss': {   'high_purity': True,
                                                                           'name': 'channel',
                                                                           'source_col': 5,
                                                                           'target_col': 6},
                                                          'node_loss': {   'group_pred_alg': 'score',
                                                                           'high_purity': True,
                                                                           'name': 'primary',
                                                                           'use_group_pred': True}},
                                'grappa_track': {   'base': {   'add_start_dir': True,
                                                                'add_start_point': True,
                                                                'node_min_size': 3,
                                                                'node_type': 1,
                                                                'start_dir_max_dist': 5},
                                                    'edge_encoder': {   'name': 'geo',
                                                                        'use_numpy': False},
                                                    'gnn_model': {   'aggr': 'add',
                                                                     'edge_classes': 2,
                                                                     'edge_feats': 19,
                                                                     'edge_output_feats': 64,
                                                                     'leakiness': 0.1,
                                                                     'name': 'meta',
                                                                     'node_classes': 2,
                                                                     'node_feats': 28,
                                                                     'node_output_feats': 64,
                                                                     'num_mp': 3},
                                                    'node_encoder': {   'name': 'geo',
                                                                        'use_numpy': False}},
                                'grappa_track_loss': {   'edge_loss': {   'high_purity': False,
                                                                          'name': 'channel',
                                                                          'source_col': 5,
                                                                          'target_col': 6}},
                                'spice': {   'fragment_clustering': {   'cluster_all': False,
                                                                        'cluster_classes': [   1],
                                                                        'min_frag_size': 10,
                                                                        'min_voxels': 2,
                                                                        'p_thresholds': [   0.95,
                                                                                            0.95,
                                                                                            0.95,
                                                                                            0.95],
                                                                        's_thresholds': [   0.0,
                                                                                            0.0,
                                                                                            0.0,
                                                                                            0.35]},
                                             'network_base': {   'data_dim': 3,
                                                                 'features': 4,
                                                                 'leakiness': 0.33,
                                                                 'spatial_size': 768},
                                             'spatial_embeddings': {   'coordConv': True,
                                                                       'embedding_dim': 3,
                                                                       'seediness_dim': 1,
                                                                       'sigma_dim': 1},
                                             'uresnet': {   'filters': 64,
                                                            'input_kernel_size': 7,
                                                            'num_strides': 7,
                                                            'reps': 2}},
                                'spice_loss': {   'embedding_weight': 1.0,
                                                  'mask_loss_fn': 'lovasz_hinge',
                                                  'min_voxels': 2,
                                                  'name': 'se_vectorized_inter',
                                                  'seediness_weight': 1.0,
                                                  'smoothing_weight': 1.0},
                                'uresnet_ppn': {   'ppn': {   'classify_endpoints': True,
                                                              'data_dim': 3,
                                                              'downsample_ghost': True,
                                                              'filters': 16,
                                                              'model_name': 'ppn',
                                                              'model_path': '/gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/weights_ppn3/snapshot-1999.ckpt',
                                                              'num_classes': 5,
                                                              'num_strides': 6,
                                                              'ppn1_size': 24,
                                                              'ppn2_size': 96,
                                                              'ppn_num_conv': 1,
                                                              'score_threshold': 0.5,
                                                              'spatial_size': 768,
                                                              'use_encoding': False,
                                                              'weight_ppn': 0.9},
                                                   'uresnet_lonely': {   'data_dim': 3,
                                                                         'features': 2,
                                                                         'filters': 16,
                                                                         'freeze': False,
                                                                         'ghost': True,
                                                                         'leakiness': 0.0,
                                                                         'num_classes': 5,
                                                                         'num_strides': 6,
                                                                         'spatial_size': 768,
                                                                         'weight_loss': True}}},
                 'name': 'full_chain',
                 'network_input': ['input_data']},
    'post_processing': None,
    'trainval': {   'checkpoint_step': 100,
                    'concat_result': [   'seediness',
                                         'margins',
                                         'embeddings',
                                         'fragments',
                                         'fragments_seg',
                                         'shower_fragments',
                                         'shower_edge_index',
                                         'shower_edge_pred',
                                         'shower_node_pred',
                                         'shower_group_pred',
                                         'track_fragments',
                                         'track_edge_index',
                                         'track_node_pred',
                                         'track_edge_pred',
                                         'track_group_pred',
                                         'particle_fragments',
                                         'particle_edge_index',
                                         'particle_node_pred',
                                         'particle_edge_pred',
                                         'particle_group_pred',
                                         'particles',
                                         'inter_edge_index',
                                         'inter_node_pred',
                                         'inter_edge_pred',
                                         'node_pred_p',
                                         'node_pred_type',
                                         'flow_edge_pred',
                                         'kinematics_particles',
                                         'kinematics_edge_index',
                                         'clust_fragments',
                                         'clust_frag_seg',
                                         'interactions',
                                         'inter_cosmic_pred',
                                         'node_pred_vtx',
                                         'total_num_points',
                                         'total_nonghost_points'],
                    'debug': False,
                    'gpus': [0],
                    'iterations': 652,
                    'log_dir': '/gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/log_trash',
                    'minibatch_size': -1,
                    'model_path': '/gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/weights_full5/snapshot-999.ckpt',
                    'optimizer': {'args': {'lr': 0.001}, 'name': 'Adam'},
                    'report_step': 1,
                    'seed': 123,
                    'train': False,
                    'unwrapper': 'unwrap_3d_scn',
                    'weight_prefix': '/gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/weights_trash/snapshot'}}
Loading file: /gpfs/slac/staas/fs1/g/neutrino/kterao/data/wire_mpvmpr_2020_04/test_08_09.root
Loading tree sparse3d_reco

Loading tree sparse3d_reco_chi2
Loading tree sparse3d_pcluster_semantics_ghost
Loading tree cluster3d_pcluster
Loading tree particle_pcluster
Loading tree particle_mpv
Loading tree sparse3d_pcluster_semantics
Loading tree sparse3d_pcluster
Loading tree particle_corrected

Sequential(
  (0): Sequential(
    (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
    (1): NetworkInNetwork64->4
  )
  (1): OutputLayer()
)
ClusterCNN(
  (input): Sequential(
    (0): InputLayer()
    (1): SubmanifoldConvolution 4->64 C7
  )
  (concat): JoinTable()
  (add): AddTable()
  (encoding_block): Sequential(
    (0): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 64->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 64->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (3): AddTable()
    )
    (1): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (3): AddTable()
    )
    (2): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 192->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 192->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (3): AddTable()
    )
    (3): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (3): AddTable()
    )
    (4): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 320->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 320->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (3): AddTable()
    )
    (5): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (3): AddTable()
    )
    (6): Sequential(
      (0): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 448->448 C3
          (2): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 448->448 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 448->448 C3
          (2): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 448->448 C3
        )
      )
      (3): AddTable()
    )
  )
  (encoding_conv): Sequential(
    (0): Sequential(
      (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 64->128 C2/2
    )
    (1): Sequential(
      (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 128->192 C2/2
    )
    (2): Sequential(
      (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 192->256 C2/2
    )
    (3): Sequential(
      (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 256->320 C2/2
    )
    (4): Sequential(
      (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 320->384 C2/2
    )
    (5): Sequential(
      (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Convolution 384->448 C2/2
    )
    (6): Sequential()
  )
  (decoding_block): Sequential(
    (0): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork768->384
        (1): Sequential(
          (0): BatchNormLeakyReLU(768,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 768->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (3): AddTable()
    )
    (1): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork640->320
        (1): Sequential(
          (0): BatchNormLeakyReLU(640,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 640->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 320->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (3): AddTable()
    )
    (2): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork512->256
        (1): Sequential(
          (0): BatchNormLeakyReLU(512,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 512->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (3): AddTable()
    )
    (3): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork384->192
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 192->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (3): AddTable()
    )
    (4): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork256->128
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (3): AddTable()
    )
    (5): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork128->64
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 64->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (3): AddTable()
    )
  )
  (decoding_conv): Sequential(
    (0): Sequential(
      (0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 448->384 C2/2
    )
    (1): Sequential(
      (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 384->320 C2/2
    )
    (2): Sequential(
      (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 320->256 C2/2
    )
    (3): Sequential(
      (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 256->192 C2/2
    )
    (4): Sequential(
      (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 192->128 C2/2
    )
    (5): Sequential(
      (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 128->64 C2/2
    )
  )
  (decoding_block2): Sequential(
    (0): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork768->384
        (1): Sequential(
          (0): BatchNormLeakyReLU(768,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 768->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->384 C3
          (2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 384->384 C3
        )
      )
      (3): AddTable()
    )
    (1): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork640->320
        (1): Sequential(
          (0): BatchNormLeakyReLU(640,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 640->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 320->320 C3
          (2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 320->320 C3
        )
      )
      (3): AddTable()
    )
    (2): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork512->256
        (1): Sequential(
          (0): BatchNormLeakyReLU(512,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 512->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->256 C3
          (2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 256->256 C3
        )
      )
      (3): AddTable()
    )
    (3): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork384->192
        (1): Sequential(
          (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 384->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 192->192 C3
          (2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 192->192 C3
        )
      )
      (3): AddTable()
    )
    (4): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork256->128
        (1): Sequential(
          (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 256->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->128 C3
          (2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 128->128 C3
        )
      )
      (3): AddTable()
    )
    (5): Sequential(
      (0): ConcatTable(
        (0): NetworkInNetwork128->64
        (1): Sequential(
          (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 128->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (1): AddTable()
      (2): ConcatTable(
        (0): Identity()
        (1): Sequential(
          (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (1): SubmanifoldConvolution 64->64 C3
          (2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
          (3): SubmanifoldConvolution 64->64 C3
        )
      )
      (3): AddTable()
    )
  )
  (decoding_conv2): Sequential(
    (0): Sequential(
      (0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 448->384 C2/2
    )
    (1): Sequential(
      (0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 384->320 C2/2
    )
    (2): Sequential(
      (0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 320->256 C2/2
    )
    (3): Sequential(
      (0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 256->192 C2/2
    )
    (4): Sequential(
      (0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 192->128 C2/2
    )
    (5): Sequential(
      (0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): Deconvolution 128->64 C2/2
    )
  )
  (outputEmbeddings): Sequential(
    (0): Sequential(
      (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): NetworkInNetwork64->4
    )
    (1): OutputLayer()
  )
  (outputSeediness): Sequential(
    (0): Sequential(
      (0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
      (1): NetworkInNetwork64->1
    )
    (1): OutputLayer()
  )
  (tanh): Tanh()
  (sigmoid): Sigmoid()
)
Total Number of Trainable Parameters = 175210432

Restoring weights for  from /gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/weights_full5/snapshot-999.ckpt...

INCOMPATIBLE KEYS!
['module.grappa_kinematics.momentum_net.linear1.weight', 'module.grappa_kinematics.momentum_net.linear1.bias', 'module.grappa_kinematics.momentum_net.norm1.weight', 'module.grappa_kinematics.momentum_net.norm1.bias', 'module.grappa_kinematics.momentum_net.norm1.running_mean', 'module.grappa_kinematics.momentum_net.norm1.running_var', 'module.grappa_kinematics.momentum_net.norm1.num_batches_tracked', 'module.grappa_kinematics.momentum_net.linear2.weight', 'module.grappa_kinematics.momentum_net.linear2.bias', 'module.grappa_kinematics.momentum_net.norm2.weight', 'module.grappa_kinematics.momentum_net.norm2.bias', 'module.grappa_kinematics.momentum_net.norm2.running_mean', 'module.grappa_kinematics.momentum_net.norm2.running_var', 'module.grappa_kinematics.momentum_net.norm2.num_batches_tracked', 'module.grappa_kinematics.momentum_net.linear3.weight', 'module.grappa_kinematics.momentum_net.linear3.bias', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.0.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.0.bias', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.0.running_mean', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.0.running_var', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.1.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.2.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.2.bias', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.2.running_mean', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.2.running_var', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.0.1.3.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.0.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.0.bias', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.0.running_mean', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.0.running_var', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.1.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.2.weight', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.2.bias', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.2.running_mean', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.2.running_var', 'module.grappa_kinematics.node_encoder.cnn_encoder.encoder.encoding_block.0.2.1.3.weight', 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make sure your module is named  
Done.
Restoring weights for ppn from /gpfs/slac/staas/fs1/g/neutrino/ldomine/chain/new/weights_ppn3/snapshot-1999.ckpt...

Done.

The output is hidden because it reprints the entire (lengthy) configuration. Feel free to take a look if you are curious!

Finally we run the chain for 1 iteration:

# Call forward to run the net, store the output in "res"
data, output = hs.trainer.forward(hs.data_io_iter)

Segmentation Accuracy: 0.9882
PPN Accuracy: 0.8556
Clustering Accuracy: 0.9465
Shower fragment clustering accuracy: 1.0000
Shower primary prediction accuracy: 0.0000
Track fragment clustering accuracy: 0.9907
Particle ID accuracy: 0.8473
Interaction grouping accuracy: 0.9609
Type accuracy: 0.7500
Vertex position accuracy: 0.6862
Vertex score accuracy: 0.9111

Now we can play with data and output to visualize what we are interested in.

entry = 0

Let us grab the interesting quantities:

clust_label = data['cluster_label'][entry]
input_data = data['input_data'][entry]
segment_label = data['segment_label'][entry][:, -1]

ghost_mask = output['ghost'][entry].argmax(axis=1) == 0
segment_pred = output['segmentation'][entry].argmax(axis=1)

Visualization of interaction clustering

clust_label_adapted = adapt_labels(output, data['segment_label'], data['cluster_label'])[entry]

clust_ids_true = get_cluster_label(torch.tensor(clust_label_adapted), output['particles'][entry], column=7)
clust_ids_pred = output['inter_group_pred'][entry]

trace = []

trace += network_topology(data['input_data'][entry][ghost_mask],
                         output['particles'][entry],
                         #edge_index=output['frag_edge_index'][entry],
                         clust_labels=clust_ids_true,
                         markersize=2, cmin=0, cmax=10, colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'True interactions'


trace+= scatter_points(clust_label_adapted,markersize=1,color=clust_label_adapted[:, 7], colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'Adapted cluster labels'

trace += network_topology(data['input_data'][entry][ghost_mask],
                         output['particles'][entry],
                         #edge_index=output['frag_edge_index'][entry],
                         clust_labels=clust_ids_pred,
                         markersize=2, cmin=0, cmax=10, colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'Predicted interactions'

fig = go.Figure(data=trace,layout=plotly_layout3d())
fig.update_layout(legend=dict(x=1.1, y=0.9))

iplot(fig)

Primary particles predictions

We need to get the true labels first:

kinematics_label = data['kinematics_label'][entry]
true_vtx, inv = np.unique(kinematics_label[:, 9:12], axis=0, return_index=True)
true_vtx_primary = kinematics_label[inv, 12]

And the predictions:

vtx_primary_pred = output['node_pred_vtx'][entry][:, 3:].argmax(axis=1)

trace = []

trace+= scatter_points(kinematics_label,markersize=1,color=kinematics_label[:, 12], colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'True vertex primary particles'

trace += network_topology(data['input_data'][entry][ghost_mask],
                         output['particles'][entry],
                         #edge_index=output['frag_edge_index'][entry],
                         clust_labels=vtx_primary_pred,
                         markersize=2, cmin=0, cmax=10, colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'Predicted vertex primary particles'

fig = go.Figure(data=trace,layout=plotly_layout3d())
fig.update_layout(legend=dict(x=1.1, y=0.9))

iplot(fig)

Particle identification (PID)

The predictions are in node_pred_type:

type_pred = output['node_pred_type'][entry].argmax(axis=1)

trace = []

trace+= scatter_points(clust_label,markersize=1,color=kinematics_label[:, -2], colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'True particle type'

trace+= scatter_points(clust_label,markersize=1,color=type_pred, colorscale=plotly.colors.qualitative.Dark24)
trace[-1].name = 'Predicted particle type'

fig = go.Figure(data=trace,layout=plotly_layout3d())
fig.update_layout(legend=dict(x=1.1, y=0.9))

iplot(fig)

Particle clustering Kinematics and particle hierarchy